£37.58

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Springer Pattern Recognition and Machine Learning Textbook

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Description

Explore the intersection of engineering and computer science with this comprehensive text from Springer. This book examines how pattern recognition and machine learning have evolved together over the last decade. It covers the transition of Bayesian methods from niche applications to mainstream use and explores graphical models as a framework for probabilistic models. Readers will learn about the development of approximate inference techniques that enhance the practical use of Bayesian methods. This resource is designed for those studying information science and statistics who need a deep understanding of these two interconnected fields.

Key Features

This Springer publication covers both pattern recognition and machine learning within the Information Science and Statistics series.

The text provides detailed information on Bayesian methods and their transition into mainstream applications.

Learn about graphical models used as a general framework for describing and applying probabilistic models.

This 760-page book offers an in-depth look at the development of approximate inference for Bayesian methods.

The perfect binding makes this a durable addition to your higher education science and technology library.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 August 2016
Listed Since
17 August 2017

Barcode

No barcode data available

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